Multi-objective optimization of engineering systems using game theory and particle swarm optimization

Kiran K. Annamdas, Singiresu S Rao

Research output: Contribution to journalArticle

25 Citations (Scopus)

Abstract

This study proposes particle swarm optimization (PSO) based algorithms to solve multi-objective engineering optimization problems involving continuous, discrete and/or mixed design variables. The original PSO algorithm is modified to include dynamic maximum velocity function and bounce method to enhance the computational efficiency and solution accuracy. The algorithm uses a closest discrete approach (CDA) to solve optimization problems with discrete design variables. A modified game theory (MGT) approach, coupled with the modified PSO, is used to solve multi-objective optimization problems. A dynamic penalty function is used to handle constraints in the optimization problem. The methodologies proposed are illustrated by several engineering applications and the results obtained are compared with those reported in the literature.

Original languageEnglish
Pages (from-to)737-752
Number of pages16
JournalEngineering Optimization
Volume41
Issue number8
DOIs
StatePublished - Aug 1 2009

Fingerprint

Game theory
Game Theory
Multiobjective optimization
Systems Theory
Systems engineering
Multi-objective Optimization
Particle swarm optimization (PSO)
Particle Swarm Optimization
Optimization Problem
Engineering
Bounce
Penalty Function
Multiobjective Optimization Problems
Engineering Application
Computational efficiency
Particle Swarm Optimization Algorithm
Computational Efficiency
Methodology
Multi-objective optimization
Particle swarm optimization

Keywords

  • Game theory
  • Multi-objective optimization
  • Particle swarm optimization

ASJC Scopus subject areas

  • Control and Optimization
  • Industrial and Manufacturing Engineering
  • Applied Mathematics
  • Computer Science Applications
  • Management Science and Operations Research

Cite this

Multi-objective optimization of engineering systems using game theory and particle swarm optimization. / Annamdas, Kiran K.; Rao, Singiresu S.

In: Engineering Optimization, Vol. 41, No. 8, 01.08.2009, p. 737-752.

Research output: Contribution to journalArticle

@article{5cadbd00399a461984427038383e1cdd,
title = "Multi-objective optimization of engineering systems using game theory and particle swarm optimization",
abstract = "This study proposes particle swarm optimization (PSO) based algorithms to solve multi-objective engineering optimization problems involving continuous, discrete and/or mixed design variables. The original PSO algorithm is modified to include dynamic maximum velocity function and bounce method to enhance the computational efficiency and solution accuracy. The algorithm uses a closest discrete approach (CDA) to solve optimization problems with discrete design variables. A modified game theory (MGT) approach, coupled with the modified PSO, is used to solve multi-objective optimization problems. A dynamic penalty function is used to handle constraints in the optimization problem. The methodologies proposed are illustrated by several engineering applications and the results obtained are compared with those reported in the literature.",
keywords = "Game theory, Multi-objective optimization, Particle swarm optimization",
author = "Annamdas, {Kiran K.} and Rao, {Singiresu S}",
year = "2009",
month = "8",
day = "1",
doi = "10.1080/03052150902822141",
language = "English",
volume = "41",
pages = "737--752",
journal = "Engineering Optimization",
issn = "0305-215X",
publisher = "Taylor and Francis Ltd.",
number = "8",

}

TY - JOUR

T1 - Multi-objective optimization of engineering systems using game theory and particle swarm optimization

AU - Annamdas, Kiran K.

AU - Rao, Singiresu S

PY - 2009/8/1

Y1 - 2009/8/1

N2 - This study proposes particle swarm optimization (PSO) based algorithms to solve multi-objective engineering optimization problems involving continuous, discrete and/or mixed design variables. The original PSO algorithm is modified to include dynamic maximum velocity function and bounce method to enhance the computational efficiency and solution accuracy. The algorithm uses a closest discrete approach (CDA) to solve optimization problems with discrete design variables. A modified game theory (MGT) approach, coupled with the modified PSO, is used to solve multi-objective optimization problems. A dynamic penalty function is used to handle constraints in the optimization problem. The methodologies proposed are illustrated by several engineering applications and the results obtained are compared with those reported in the literature.

AB - This study proposes particle swarm optimization (PSO) based algorithms to solve multi-objective engineering optimization problems involving continuous, discrete and/or mixed design variables. The original PSO algorithm is modified to include dynamic maximum velocity function and bounce method to enhance the computational efficiency and solution accuracy. The algorithm uses a closest discrete approach (CDA) to solve optimization problems with discrete design variables. A modified game theory (MGT) approach, coupled with the modified PSO, is used to solve multi-objective optimization problems. A dynamic penalty function is used to handle constraints in the optimization problem. The methodologies proposed are illustrated by several engineering applications and the results obtained are compared with those reported in the literature.

KW - Game theory

KW - Multi-objective optimization

KW - Particle swarm optimization

UR - http://www.scopus.com/inward/record.url?scp=70449602576&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=70449602576&partnerID=8YFLogxK

U2 - 10.1080/03052150902822141

DO - 10.1080/03052150902822141

M3 - Article

AN - SCOPUS:70449602576

VL - 41

SP - 737

EP - 752

JO - Engineering Optimization

JF - Engineering Optimization

SN - 0305-215X

IS - 8

ER -